基于M-树的道路网精确k-灵活聚集最近邻搜索
Efficient Exact k-Flexible Aggregate Nearest Neighbor Search in Road Networks Using the M-tree
摘要：
提出了一种基于M树的精确k-柔性聚集最近邻搜索算法。最先进的IER-kNN算法使用R树，并根据道路网络中对象的欧几里德坐标修剪掉不必要的节点。然而，IER-kNN对索引节点进行了许多不必要的访问，因为对象之间的欧氏距离与它们之间的实际最短路径距离有很大差异。相比之下，我们在这项研究中提出的算法可以大大减少不必要的访问索引节点相比，IER-kNN，因为M-树是基于对象之间的实际最短路径距离。据我们所知，我们的算法是第一个精确的FANN算法，使用的M-树。我们证明，我们的算法不会导致任何错误的下降。在使用各种真实道路网络数据集进行的一系列实验中，我们的算法始终优于IER-kNN高达6.92倍。
英文摘要：
This study proposes an efficient exact k-flexible aggregate nearest neighbor (k-FANN) search algorithm in road networks using the M-tree. The state-of-the-art IER-kNN algorithm used the R-tree and pruned off unnecessary nodes based on the Euclidean coordinates of objects in road networks. However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distances between objects are significantly different from the actual shortest-path distances between them. In contrast, our algorithm proposed in this study can greatly reduce unnecessary accesses to index nodes compared with IER-kNN since the M-tree is constructed based on the actual shortest-path distances between objects. To the best of our knowledge, our algorithm is the first exact FANN algorithm that uses the M-tree. We prove that our algorithm does not cause any false drop. In conducting a series of experiments using various real road network datasets, our algorithm consistently outperformed IER-kNN by up to 6.92 times.
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